Generative AI has completely changed the way we live and work. In 2022, OpenAI introduced ChatGPT, which could mimic emotions, write code and solve complex queries. By March 2023, the release of GPT-4 expanded its capabilities to analyze image inputs - this was another milestone in AI usage.

Now, this technology is used in many industries, including education, healthcare, finance, and automotive. For example:

• A skin vision app uses generative AI to detect early signs of cancer.
• In 2024, Proto and AWS introduced a text-to-hologram solution that wowed the entire tech sector.

You can use this to create animated holograms in just two minutes that respond to humans in real time. But these are just a few examples - there's so much more to it. So, let's explore this technology in detail.

Defining generative AI

Short for Generative AI, Gen AI is a type of artificial intelligence that creates new content, such as text, images, music, audio, and videos. It learns patterns from an existing dataset and gives a unique output.

For example, if you train a model on images of human faces, it can generate highly realistic faces that don't belong to real people.

Image by Laiba Siddiqui

In October 2024, Apple also integrated generative AI into iOS 18.1, iPadOS 18.1, and macOS Sequoia, reaching millions of users with this update. They introduced an AI-powered Siri, which can perform advanced tasks like summarizing notifications and helping users find anything in their gallery by just describing it - and all of this is done without compromising security.

For example, you can ask it to provide your passport number while booking a flight and it will do the work for you.

How generative AI works

A lot of us think traditional chatbots like Siri are actual examples of generative AI - but that's not quite it. Siri retrieves information from its database and does not create new stuff.

Generative AI, on the other hand, learns patterns from large datasets to create unique content. To achieve this, it relies on three core models-GANs, VAEs, and transformer-based - and each one works differently. So, let's see how they work.

Generative adversarial networks (GANs)

GANs are a type of machine learning model based on a two-player zero-sum game structure. They consist of two neural networks that work together to produce realistic outputs. Here's how:

• Generator creates new content by learning patterns from training data.
• Discriminator evaluates the generated content and determines whether it is real (from the training data) or fake (created by the Generator).

GANs have gained immense popularity in creative industries, especially among artists and designers, due to their ability to generate creative content.

One example of GANs is the creation of Portrait of Edmond de Belamy by the French group Obvious. It was trained on a dataset of 15,000 portraits from the 14th to 20th centuries and was sold at auction for $432,500 in 2018.

Portrait of Edmond de Belamy portrait. (Source: Christies)

Variational autoencoders (VAE)

VAE is another type of Gen AI model that consists of two components: an encoder and a decoder. Here's how they work together:

• Encoder compresses input data into a simplified representation.
• Decoder reconstructs data from this simplified representation and adds details to produce a new output.

They work like an artist who begins with a rough sketch (this would be encoder) and then creates a complete picture by the end (this would be the decoder). This process allows VAEs to learn patterns in the data and generate realistic outputs based on the learned structure.

VAEs are primarily used in fraud and cyberattack detection. They recognize patterns that deviate from normal behavior to alert companies of fraud. For example, you can train it on normal transactions to identify anomalies such as credit card fraud.

Transformer based models

Transformer-based models, including Chat-GPT4, are specifically used for tasks like natural language processing (NLP) and data analysis. They rely on a self-attention mechanism to understand relationships across large amounts of data by focusing on the most relevant parts of the input.

Unlike earlier models, transformers can analyze entire sentences simultaneously to understand connections and nuances in text. This makes them highly helpful for sentiment analysis and text summarization. Since they can analyze patterns and connections in the language, transformers are used widely to detect fake news on social media.

Benefits of generative AI

The latest enhancements in generative AI have incredible implications and applications. Some organizations have already started implementing new initiatives. Let's see what the key benefits are:

Better customer experience

Businesses relied on basic chatbots that only gave pre-programmed answers. Now, they incorporate generative AI into their systems, which helps to:

• Understand customer needs.
• Provide personalized experiences.

For example, Gen AI can analyze a customer's purchase history and usage patterns to give tailored recommendations. That's a primary reason why 46% of businesses in the retail industry use it for personalized marketing and advertising. This shift shows how much value companies now place on creating meaningful and customized interactions with their customers.

(Related reading: what is customer experience & CX metrics to know.)

Improved decision-making

Poor decision-making costs companies 3% of their profits on average. But now, Gen AI helps businesses and individuals make informed decisions. For example, tools like Vantiq can predict floods and reroute traffic. So, in case of an emergency, these tools can estimate the resources needed to handle the situation.

Efficient education system

Gen AI has replaced the traditional one-size-fits-all approach in the education sector with personalized learning solutions. In 2024, 63% of U.S. teenagers used AI chatbots and text generators to complete their school assignments.

Not only are students using Gen AI to their advantage, but teachers are also using it to their advantage. Mary Alice Hudson, lead media coordinator at a North Carolina school district, used this to create acronyms or songs to help students more easily understand new topics.

More job opportunities

The job market has been shifting since the rise of generative AI but it's not something to fear. The U.S. job postings mentioning generative AI tripled between September 2023 and September 2024. This adoption rate is even higher in other parts of the world.

So, if you stay up to date with the latest technologies and keep building the relevant skills, you can acquire a lot of great job opportunities.

Risks with generative AI

While generative AI provides many benefits, it also presents potential ethical concerns. (And that's why so many leaders are calling for AI governance and AI ethics.

Let's understand some of these risks, understanding that some risks are not clear yet, as we're still in the early days of widespread AI usage.

Social isolation

As generative AI evolves, the use of AI companion apps has skyrocketed. A recent example is the 2,400% increase in Google searches for AI girlfriend in 2024 - this shows the growing interest in these technologies.

However, this trend has sparked concerns about its impact on human relationships too. For example, when a Reddit user shared images of his AI-generated wife and children, many questions were raised about the societal effects of such technologies. Simply put, these apps can contribute to social isolation because users may end up replacing genuine human connections with AI-based interactions.

Personalized cyberattacks

Some people are using this technology to harm others as well. For example, ChatGPT has a feature that allows users to build custom AI assistants. While this feature has legitimate uses, attackers can exploit it to develop tools for cybercrime.

Recently BBC conducted an experiment with an AI bot called Crafty Emails to generate phishing emails. In one case, the bot created a message pretending to be a distressed daughter asking her mother for money for a taxi. This was an emotionally manipulative tactic to trick the recipient into clicking harmful links.

It shows how Gen AI can be a huge cybersecurity concern.

Misuse of information

Malicious actors can exploit Gen AI to manipulate public perception by spreading false information through deepfake videos and audio. For example, during the 2024 U.S. elections, a fake image of Taylor Swift endorsing Donald Trump went viral even though she was publicly backing candidate Vice President Kamala Harris.

Generative AI calls for stricter security measures

Generative AI has taken human creativity to another level. However, this technology is relatively new, so the risks and opportunities it presents will likely evolve in the future.

As its integration into society and business grows, it will show more benefits and risks. So, we should focus on creating stricter regulations to ensure public safety and security.

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